Over the last 60 to 70 years, the contribution of plant breeding to agricultural productivity has been spectacular (Smith (1998) 53rd Annual corn and sorghum research conference, American Seed Trade Association, Washington, D.C.; Duvick (1992) Maydica 37: 69). This has happened in large part because plant breeders have been adept at assimilating and integrating information from extensive evaluations of segregating progeny derived from multiple crosses of elite, inbred lines. Conducting such breeding programs requires extensive resources. A commercial maize breeder, for example, may evaluate 1,000 to 10,000 F3 topcrossed progeny derived from 100 to 200 crosses in replicated field trials across wide geographic regions. Despite such significant investments of resources, there is evidence that the gains of the past will be difficult to sustain with current methods (Smith (1998), supra).
The primary motivation for developing molecular marker technologies from the point of view of plant breeders has been the possibility to increase breeding efficiency through marker assisted selection (MAS). The key components to the implementation of this approach are: (i) the creation of a dense genetic map of molecular markers, (ii) the detection of quantitative trait loci (QTL) based on statistical associations between marker and phenotypic variability, (iii) the definition of a set of desirable marker alleles based on the results of the QTL analysis, and (iv) the use and/or extrapolation of this information to the current set of breeding germplasm to enable marker-based selection decisions to be made. To date, this approach has been effective for relatively simple traits that are controlled by a small number of genes (e.g. disease resistance; Flint-Garcia et al., (2003) Theor. Appl. Genet. 107:1331-1336) but less effective for more complex traits that are controlled by many genes that are under the influence of epistasis (gene-by-gene interaction) and gene-by-environment interaction effects (Openshaw & Frascaroli (1997) Proc. Annu. Corn Sorghum Res. Conf. 52:44-53; Melchinger et al. (1998) Genetics 149:383-403; Utz et al. (2000) Genetics 154:1839-1849).
Conventional mapping approaches are typically predicated on the assumption that any QTL act in an additive manner, assuming that the effects of epistasis and genotype x environment interactions are negligible or nonexistent (for a recent review see, Bernardo, R. (2001) What if we knew all the genes for a quantitative trait in hybrid crops? Crop Science 41:1-4). In the absence of epistasis, there is no advantage to marker assisted selection for a quantitative trait (that is, knowing the effects of all genes contributing to the trait) over phenotypic selection. However, current understanding suggests that context dependent factors, such as epistasis, are important aspects of the genetic architecture of quantitative traits (See, e.g., Holland, J. B., Epistasis and Plant Breeding (2001) Plant Breeding Reviews 21:27-92).
A number of factors increase the difficulty of successfully employing a marker-based selection scheme for complex traits. One major problem has been the effective detection, estimation and utility of QTL and their effects. This is especially the case for traits governed by “context dependent” gene effects (i.e. interaction with other genes and/or environment).
Analysis methods have been developed in an attempt to address the effects of context dependency (e.g., Crossa et al. (1999) Theor. Appl. Genet. 99:611-625; Jannink & Jansen (2001) Trends Plant Sci. 6:337-342; Nelson et al. (2001) Genome Research 11:458-470; Boer et al. (2002) Genetics 162: 951-960; van Eeuwijk et al. (2002) In Kang, M. S. (ed). Quantitative Genetics, Genomics and Plant Breeding. pp. 245-256. CAB International, Wallingford). For example, in the case of epistasis, Holland (2001; Plant Breeding Reviews 21:27-92) outlined an approach that was based on the identification of preferred allele configurations across interacting genes. Similar approaches have been suggested by others (e.g., Jansen et al. (2003) Crop Sci. 43:829-834; Kuhnlein et al. (2003) Poultry Science 82: 876-881). Other advances in methodology include the use of multiple line crosses among related individuals (Jannink et al. (2001) Genetics 157:445-454; Yi and Xu (2001) Genetics 157:1759-1771; Bink et al. (2002) Theor. Appl. Genet 103:1243-1253) and/or haplotype information to increase the power to accurately estimate QTL and their effects (Meuwissen and Goddard (2000) Genetics 155:421-430; Jansen et al. (2003) Crop Sci. 43:829-834). In all cases, the analysis methods assume that the mapping studies can be conducted with sufficient power to adequately account for all, or at least the important, context dependencies that may exist.
Regardless of what assumptions are made, a common outcome of all QTL analysis methods is the estimation of QTL allele effects, whether at an individual gene level or across multiple interacting gene complexes (Jansen (1996) Trends in Plant Science 1:89-94). A target combination of marker alleles is defined from these estimates, forming the basis of selection in the application of MAS in a breeding program. More advanced applications of MAS may weight specific marker alleles based on the amount of genetic variation they explain in the analysis (Lande and Thompson (1990) Genetics 124:743-756).
However, in essence, the approach to MAS in plant breeding has been to develop accurate estimates of QTL effects within a relatively narrow reference population and use those estimates in the application of marker-based selection. This approach assumes that the desirable QTL alleles once identified will remain relevant over many cycles of selection. That is, the estimates of QTL effects that are calculated at the beginning will still apply as new germplasm is created during the breeding process (e.g., Peleman & Rouppe van der Voort (2003) Trends Plant Sci. 8:330-334). Additional QTL analyses may be conducted on new germplasm, but the purpose of such an approach is to validate or refine the initial estimates by making them ‘more accurate’. The assumption that the value of QTL alleles should stay relatively fixed or static is appropriate for traits controlled solely by additive genes (e.g., Bernardo (2001) Crop Sci. 41:1-4). In this way, the effects of QTL are consistent across all or most germplasm (both current and future) and hence MAS can be implemented by independently assembling or ‘stacking’ desirable alleles. However, when context dependencies are present, the value of QTL alleles can differ depending on the genetic structure of the current set of germplasm in the breeding program (Wade (2002) J. Evol. Biology 15:337-346). That is, the value of a given QTL allele can change over cycles of selection due to changes in the background (i.e. context dependent) effects at any given time in the breeding process. Therefore, when these background effects are important, the stacking of desirable alleles by MAS becomes inadequate because it is possible that the initial target combination of alleles is no longer the best target, or even a relevant target, for increased trait performance in subsequent breeding cycles.
The methods of the present invention provide a novel approach, designated “Mapping As-You-Go” that are applicable, not only where the target genotype can be defined prior to selection, but also in situations in which it is not possible to define the target genotype at the commencement of the breeding program; the definition of the target genotype will be developed and refined with each cycle of selection in the breeding program. Thus, the definition of the target genotype can change with time as selection changes the genetic structure of the breeding population. These and other features will be apparent upon complete review of the following.